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The resource-based view (RBV) of the firm focuses on how firm-level assets and capabilities influence firm performance. Scholars have noted the need for studies grounded…
The resource-based view (RBV) of the firm focuses on how firm-level assets and capabilities influence firm performance. Scholars have noted the need for studies grounded in the RBV to account for the role of the strategic group level, but uncertainty remains about how to do so. Random coefficients modeling (RCM) provide an appropriate technique to integrate these two levels of analysis, but its use has been limited in strategic management research to date. I review research integrating firm and strategic group levels and provide a roadmap for future research seeking to integrate these two levels’ influences on firm performance, and use RCM to illustrate the effects of firm resources on performance under three depictions of the strategic group level culled from strategic management research. Findings suggest that interpretations about the efficacy of resources’ influence on performance vary considerably across methodological specification. Next, I use RCM to illustrate how strategic management researchers can further integrate the firm and group levels by demonstrating how variables at the group level of analysis may interact with firm-level characteristics. I conclude with suggestions for future research using RCM to integrate the strategic group into multilevel studies predicting firm performance.
Designing a top-quality research study is a complex challenge. We introduce the research design canvas as a tool to help researchers manage this complexity and create…
Designing a top-quality research study is a complex challenge. We introduce the research design canvas as a tool to help researchers manage this complexity and create better studies. Completing a research design canvas requires researchers to a priori articulate one or more research questions, identify a gap in the literature in need of closing, select a guiding theory or theories, construct hypotheses, choose a sample, select measures, recognize boundary conditions, plot a data analysis strategy, ascertain a study’s limitations, clarify the value added of key research partners, pinpoint a target journal for potential publication, and think ahead about their backup plans in case the research process goes awry. Many studies are doomed to bad fates by poor decisions about these elements. Using the research design canvas can help researchers ensure that they have selected a valid approach to each element, reason through the interrelationships among each decision point within the canvas to ensure that there is consistency among them, and critically assess whether the design as a whole provides the basis for a new advance. By doing so, scholars may be able to improve their chances of defying the high rejection rates of leading journals.